1. Field of the Invention
The present invention generally relates to resistive electronic circuit elements and, in particular, to resistance elements in which resistance may be controlled electronically.
2. Description of the Prior Art
Perhaps the most basic of all electronic circuit elements is the resistor and the property of resistance is inherent in all materials capable of carrying an electric current. So-called linear networks are comprised entirely of interconnected resistances. Such linear networks are often used for generating a plurality of differing voltages from one or more voltages which may be applied at different parts of the network. More generally, the resistance values placed in a circuit may affect any electronic circuit in many ways, such as establishing the gain of an amplifier, the time constant of a delay or filter circuit or the response of a feedback circuit, which, in turn, may have applicability to a wide variety of circuits such as digital to analog converters, oscillators, tuners, threshold circuits and the like.
Resistive circuit elements (e.g. resistors) generally have a resistance element formed of a material such as carbon which has a highly predictable specific resistance. The desired resistance value is then obtained by alteration of the geometry of the resistance element. Increasing the length of the resistance element between highly conductive terminals attached thereto will increase the resistance value. Increasing cross-sectional area of the element between such terminals will decrease resistance and increase the ability of the resistance element to dissipate heat. The accuracy of the geometry of the resistance element will determine the accuracy of the resistance value. For this reason, also, it is very difficult to alter the resistance value without human intervention. Moreover, it is impossible to reversibly alter the resistance value of the resistance element of this type of resistor. Therefore, this type of resistance element is generally referred to as a fixed resistor and alteration of the resistance value requires irreversible physical trimming or removal and substitution of such fixed resistors.
As a practical matter, however, the formation of high accuracy resistors is expensive. In many applications, high accuracy of resistance values is not required and tolerances of 5% and 10% are common. It is also possible to design circuits so that a large plurality of low tolerance network elements can be adequately compensated by a small number of variable resistors.
The typical form of known variable resistor is known as the potentiometer and takes the form of a resistance element and a wiper element which makes contact with the resistance element at an adjustable location along its length to determine the value of the resistance which will be placed in the circuit or to establish a ratio of resistance values having a fixed sum. While this is satisfactory for many applications, the adjustment of the resistance value is mechanical and insusceptible of electronic control without resort to servo systems and the like. Also, both the resistance element and the wiper element are subject to wear, tending to alter the resistance value of the resistance element and reduce the reliability of the circuit. Therefore, use of such variable resistors is preferably limited to applications where convenient manual control is necessary or changes of resistance value will not often be required.
Electronic control of resistance value may therefore be desirable to enhance the speed and accuracy of adjustment of resistor value and also to allow remote control of the resistance where the resistance may be inaccessible, as in airborne systems or particularly large or complex circuits or systems. The ability to electronically alter resistance values is often a key to design of adaptive circuits which can be arranged to alter function based on the nature of the input signal, often under computer control.
A particular type of adaptive circuit which has been the subject of recent interest is the so-called artificial neural network (ANN). Neural networks attempt to model the ability of the human brain to learn in order to solve problems which are difficult for conventional computer programs. In fact, the difficulty in the application of conventional computers may lie in the lack of consistent knowledge of the problem to be solved, the lack of a known algorithm for solving the problem or, where the solution is highly dependent on the input information, the lack of knowledge of the nature of input data. Such problems are typically encountered in speech or pattern recognition, image processing and vehicle guidance. However, neural networks can have applicability to virtually any type of application where it may be desired to alter the function performed in a manner which is wholly or partially determined by accumulated information. Neural networks can accumulate and generalize input patterns until they develop synapse weight values, collectively resulting in algorithms which determine solutions to the problem.
Neural networks, in order to simulate learning of complex problems, rely upon a characteristic highly parallel structure. Despite the potential of neural networks and the amount of effort which has recently been expended in their development, neural networks have not been particularly successful in practice. When computer control or simulation of neural networks with a high degree of parallelism is done, the sequential nature of digital computers results in poor response times. When parallel processing is attempted with massive cellular arrays of processors, the amount of hardware required is prohibitive for all but the simplest of applications. For instance, hundreds of thousands of processing elements may be necessary to perform pattern recognition tasks of routine complexity. The hardware requirements cannot easily be met since such numbers of processing elements dictate replication of processors at the chip level by VLSI techniques. No suitable structure for providing local memory, developing a synapse weight value and modulating the incoming signals in accordance with the synapse weight has been developed which could be integrated on a semiconductor chip.
Digital designs of neural networks have the advantage of having good noise immunity, tolerance for differences from chip to chip and ease of interfacing with digital computing machinery and digital communication networks. However, digital implementations for neural networks have much larger and more complicated designs than analog implementations. Digital implementations also typically require much greater bandwidth and are inherently much slower than analog implementations due to sequential iteration of many parallel paths.
Analog designs, while offering greater simplicity and speed of operation, usually rely on sample-and-hold weight circuits with continuous voltage or current levels as inputs. These sample-and-hold circuits require circuitry to provide periodic refresh and large chip areas for storage capacitors. Sample-and-hold circuits are also sensitive to noise and chip-to-chip variations of devices. For this reason, analog implementations of neural networks remain difficult to design and realize.